429 citations · 880 across the 60 of their papers we have counts for
11 papers · 1 filter
Urban Radiance Fields
Konstantinos Rematas, Andrew Liu, Pratul P. Srinivasan +4
The goal of this work is to perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environ…
Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos
Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari +1
We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video. To this end, we model the blurred appearance of a…
Transferability Metrics for Selecting Source Model Ensembles
Andrea Agostinelli, Jasper Uijlings, Thomas Mensink +1
We address the problem of ensemble selection in transfer learning: Given a large pool of source models we want to select an ensemble of models which, after fine-tuning on the targe…
Transferability Estimation using Bhattacharyya Class Separability
Michal Pándy, Andrea Agostinelli, Jasper Uijlings +2
Transfer learning has become a popular method for leveraging pre-trained models in computer vision. However, without performing computationally expensive fine-tuning, it is difficu…
Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo
Francesco Sarno, Suryansh Kumar, Berk Kaya +3
We present an automated machine learning approach for uncalibrated photometric stereo (PS). Our work aims at discovering lightweight and computationally efficient PS neural network…
Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo
Berk Kaya, Suryansh Kumar, Francesco Sarno +2
We present a modern solution to the multi-view photometric stereo problem (MVPS). Our work suitably exploits the image formation model in a MVPS experimental setup to recover the d…